Radiomics is a field of study where images have great potential for precision and personalized medicine [1]}, [2]}. It is defined as the extraction of a large number of features from medical images such as computed tomography (CT), magnetic resonance imaging (MRI) or positron emission tomography (PET) [3]}. The first step in radiomic analysis in oncology is the lesion segmentation (Figure REF ). This task requires a highly trainable physician, is time consuming and the ground truth defined is physician subjective. Recently, deep learning showed very promising results in image classification [4]}, object detection [5]}, and image segmentation [6]}. In the medical imaging field, various applications have emerged in different areas, including pathology classification [7]}, treatment response prediction [8]}, lesions segmentation [9]} and organs at risk segmentation [10]}. Thus, artificial intelligence in general and deep learning in particular can come in handy to develop computer aided diagnostic applications (CAD). However, deep learning approaches are well known for their data hungry nature, and annotated data are usually hard to obtain in the medical imaging field. In a recent work, we tried to tackle this problem with a weakly supervised learning strategy to segment the lesions, and then predict the outcome. This approach showed very promising results outperforming state-of-the-art supervised approaches such as U-Net [11]} for image segmentation, and comparable results with supervised learning [12]} for radiomics analysis. However, the drawback of this method is the two-stage segmentation-outcome prediction. In addition, recent studies have shown the potential of peritumoral regions on boosting the accuracy of outcome prediction [13]}, [14]}, [15]}. Thus, the association of the intratumoral and peritumotal regions provides rich information for radiomic analysis [16]}, [17]}.
